In this project, the PASCAL VOC 2012 dataset is used to train a YOLOv8 object detection model. This model is capable of identifying and classifying various objects in real-time, such as cars, animals, and people. The goal is to build a system that can automatically detect and classify objects in images, which can be applied in fields like surveillance, autonomous driving, and industrial automation.
Object detection is a key technology for many real-world applications, from self-driving cars to security systems. By training a model to detect objects in images, you can:
This project provides the foundation for developing systems capable of identifying objects quickly and accurately in a variety of scenarios.
Annotations in the PASCAL VOC dataset, originally in XML format, are converted to YOLO format. In this format:
.txt file containing:center_x, center_y, width, height)To ensure the model generalizes well, subsets of images and annotations are selected randomly for training and validation. This step helps prevent overfitting by exposing the model to a wide variety of examples.
Once subsets are selected, the corresponding image and annotation files are organized into directories specifically for training and validation. This structure simplifies the training process.
Before training, it’s essential to analyze the dataset's class distribution. This ensures the dataset is balanced, reducing potential biases that could affect the model's performance.
Visualization techniques such as pie charts and bar charts are used to display the distribution of object classes in the dataset. This helps in identifying imbalances and making informed decisions about preprocessing.
A YAML configuration file is created to specify:
This file guides the YOLO model in processing the dataset effectively.
Using the prepared dataset, the YOLOv8 model is trained. Transfer learning is applied, fine-tuning the model for 25 epochs. Parameters like image size and batch size are optimized for better performance.
After training, the model's performance is evaluated using key metrics such as:
These metrics provide insights into how well the model detects and classifies objects in the validation dataset.
After completing the training, the model’s performance on the validation set was as follows:
These results indicate that the model can detect objects with high precision, though there is room for improvement in recall and overall performance.
pip install ultralytics
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import cv2
import numpy as np
import matplotlib.pyplot as plt
from ultralytics import YOLO
import torchvision
from torchvision import datasets, transforms
import os
import random
import xml.etree.ElementTree as ET
import shutil
Creating new Ultralytics Settings v0.0.6 file ✅ View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json' Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.
VOC_CLASSES = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
'train', 'tvmonitor']
def convert_voc_to_yolo(voc_dir, output_dir):
os.makedirs(output_dir, exist_ok=True)
# Iterating Over Annotation Files
for xml_file in os.listdir(os.path.join(voc_dir, 'Annotations')):
tree = ET.parse(os.path.join(voc_dir, 'Annotations', xml_file))
root = tree.getroot()
img_width = int(root.find('size/width').text)
img_height = int(root.find('size/height').text)
yolo_annotation = []
for obj in root.findall('object'):
class_id = VOC_CLASSES.index(obj.find('name').text)
bbox = obj.find('bndbox')
xmin, ymin, xmax, ymax = [float(bbox.find(tag).text) for tag in ['xmin', 'ymin', 'xmax', 'ymax']]
x_center = (xmin + xmax) / 2 / img_width
y_center = (ymin + ymax) / 2 / img_height
width = (xmax - xmin) / img_width
height = (ymax - ymin) / img_height
yolo_annotation.append(f"{class_id} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}")
# saving the converted annotations to a text file.
with open(os.path.join(output_dir, f"{root.find('filename').text.split('.')[0]}.txt"), 'w') as f:
f.write("\n".join(yolo_annotation))
# Setting directory paths & calling the convert method
voc_dir = "/kaggle/input/pascal-voc-2012/VOC2012"
output_dir = "/kaggle/working/VOCdevkit/VOC2012/labels"
convert_voc_to_yolo(voc_dir, output_dir)
import os
import random
import shutil
from PIL import Image
# Path to directories
voc_images_dir = "/kaggle/input/pascal-voc-2012/VOC2012/JPEGImages"
voc_labels_dir = "/kaggle/working/VOCdevkit/VOC2012/labels"
imagesets_dir = "/kaggle/input/pascal-voc-2012/VOC2012/ImageSets/Main"
subset_train_dir = "/kaggle/working/VOCdevkit/VOC2012/train"
subset_val_dir = "/kaggle/working/VOCdevkit/VOC2012/val"
# Create directories for the subset
os.makedirs(os.path.join(subset_train_dir, "images"), exist_ok=True)
os.makedirs(os.path.join(subset_train_dir, "labels"), exist_ok=True)
os.makedirs(os.path.join(subset_val_dir, "images"), exist_ok=True)
os.makedirs(os.path.join(subset_val_dir, "labels"), exist_ok=True)
# Function to select a random subset
def select_random_subset(list_file, subset_size):
with open(list_file, 'r') as f:
file_list = f.read().strip().split()
random.shuffle(file_list)
return file_list[:subset_size]
# Function to copy images and labels and return image paths
def copy_files_from_list(file_list, source_img_dir, source_lbl_dir, dest_img_dir, dest_lbl_dir):
image_paths = []
for file_name in file_list:
img_file = file_name + ".jpg"
lbl_file = file_name + ".txt"
# Copy image
shutil.copy(os.path.join(source_img_dir, img_file), os.path.join(dest_img_dir, img_file))
# Copy corresponding label
shutil.copy(os.path.join(source_lbl_dir, lbl_file), os.path.join(dest_lbl_dir, lbl_file))
# Append image path
image_paths.append(os.path.join(dest_img_dir, img_file))
return image_paths
# Define the subset size
train_subset_size = 3000
val_subset_size = 300
# Select random subsets
train_files_subset = select_random_subset(os.path.join(imagesets_dir, "train.txt"), train_subset_size)
val_files_subset = select_random_subset(os.path.join(imagesets_dir, "val.txt"), val_subset_size)
# Copy training subset to the directories
train_image_paths = copy_files_from_list(train_files_subset, voc_images_dir, voc_labels_dir,
os.path.join(subset_train_dir, "images"), os.path.join(subset_train_dir, "labels"))
# Copy validation subset to the directories
val_image_paths = copy_files_from_list(val_files_subset, voc_images_dir, voc_labels_dir,
os.path.join(subset_val_dir, "images"), os.path.join(subset_val_dir, "labels"))
print("done")
# Print sample image paths
print("Sample training image paths:")
for image_path in train_image_paths[:2]:
print(image_path)
print("Sample validation image paths:")
for image_path in val_image_paths[:2]:
print(image_path)
done Sample training image paths: /kaggle/working/VOCdevkit/VOC2012/train/images/2009_003808.jpg /kaggle/working/VOCdevkit/VOC2012/train/images/2009_002912.jpg Sample validation image paths: /kaggle/working/VOCdevkit/VOC2012/val/images/2009_000472.jpg /kaggle/working/VOCdevkit/VOC2012/val/images/2009_001648.jpg
import os
import shutil
import random
from collections import defaultdict
# Path to directories
voc_images_dir = "/kaggle/input/pascal-voc-2012/VOC2012/JPEGImages"
voc_labels_dir = "/kaggle/working/VOCdevkit/VOC2012/labels"
imagesets_dir = "/kaggle/input/pascal-voc-2012/VOC2012/ImageSets/Main"
subset_train_dir = "/kaggle/working/VOCdevkit/VOC2012/train"
subset_val_dir = "/kaggle/working/VOCdevkit/VOC2012/val"
# Create directories for the subset
os.makedirs(os.path.join(subset_train_dir, "images"), exist_ok=True)
os.makedirs(os.path.join(subset_train_dir, "labels"), exist_ok=True)
os.makedirs(os.path.join(subset_val_dir, "images"), exist_ok=True)
os.makedirs(os.path.join(subset_val_dir, "labels"), exist_ok=True)
# Function to select a random subset
def select_random_subset(list_file, subset_size):
with open(list_file, 'r') as f:
file_list = f.read().strip().split()
random.shuffle(file_list)
return file_list[:subset_size]
# Function to copy images and labels and return image paths
def copy_files_from_list(file_list, source_img_dir, source_lbl_dir, dest_img_dir, dest_lbl_dir):
image_paths = []
for file_name in file_list:
img_file = file_name + ".jpg"
lbl_file = file_name + ".txt"
# Copy image
shutil.copy(os.path.join(source_img_dir, img_file), os.path.join(dest_img_dir, img_file))
# Copy corresponding label
shutil.copy(os.path.join(source_lbl_dir, lbl_file), os.path.join(dest_lbl_dir, lbl_file))
# Append image path
image_paths.append(os.path.join(dest_img_dir, img_file))
return image_paths
# Define the subset size
train_subset_size = 3000
val_subset_size = 300
# Select random subsets
train_files_subset = select_random_subset(os.path.join(imagesets_dir, "train.txt"), train_subset_size)
val_files_subset = select_random_subset(os.path.join(imagesets_dir, "val.txt"), val_subset_size)
# Copy training subset to the directories
train_image_paths = copy_files_from_list(train_files_subset, voc_images_dir, voc_labels_dir,
os.path.join(subset_train_dir, "images"), os.path.join(subset_train_dir, "labels"))
# Copy validation subset to the directories
val_image_paths = copy_files_from_list(val_files_subset, voc_images_dir, voc_labels_dir,
os.path.join(subset_val_dir, "images"), os.path.join(subset_val_dir, "labels"))
# Function to count images per class
def count_images_per_class(labels_dir):
class_count = defaultdict(int) # Initialize a dictionary to count classes
# Loop through label files
for label_file in os.listdir(labels_dir):
if label_file.endswith('.txt'):
with open(os.path.join(labels_dir, label_file), 'r') as f:
# Read all lines in the label file
lines = f.readlines()
# Count occurrences of each class
for line in lines:
# Split the line and take the first value as class ID
class_id = int(line.strip().split()[0]) # Get the class ID from the first value
class_count[class_id] += 1
return class_count
class_names = [
"person", "bird", "car", "cat", "dog",
"horse", "sheep", "aeroplane", "bicycle",
"boat", "bus", "motorbike", "train", "cow",
"elephant", "bear", "giraffe", "zebra",
"sofa", "tvmonitor"
]
# Count images per class for training and validation
train_class_counts = count_images_per_class(os.path.join(subset_train_dir, "labels"))
val_class_counts = count_images_per_class(os.path.join(subset_val_dir, "labels"))
# Print counts
print("Training images per class:")
for class_id, count in train_class_counts.items():
print(f"{class_names[class_id]}: {count}")
print("\nValidation images per class:")
for class_id, count in val_class_counts.items():
print(f"{class_names[class_id]}: {count}")
print("done")
Training images per class: bird: 299 train: 286 elephant: 3891 car: 449 aeroplane: 481 person: 342 cow: 272 sofa: 259 sheep: 930 giraffe: 394 horse: 254 cat: 397 motorbike: 566 boat: 266 bicycle: 1137 bus: 297 bear: 435 dog: 598 tvmonitor: 329 zebra: 322 Validation images per class: cat: 44 person: 59 elephant: 473 zebra: 32 cow: 35 sofa: 41 motorbike: 86 train: 50 sheep: 92 bear: 24 dog: 65 bus: 33 boat: 49 tvmonitor: 47 aeroplane: 61 horse: 23 car: 51 bicycle: 131 giraffe: 48 bird: 35 done
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Data for training and validation images per class
train_data = {
'Object Class': ['sofa', 'bicycle', 'horse', 'train', 'elephant', 'bird', 'bear', 'aeroplane',
'tvmonitor', 'sheep', 'dog', 'motorbike', 'bus', 'cat', 'car', 'zebra',
'cow', 'person', 'boat', 'giraffe'],
'Count': [314, 1384, 307, 364, 4829, 391, 531, 586, 403, 1138, 730, 732, 359, 496, 565, 380, 358, 450, 343, 492]
}
val_data = {
'Object Class': ['horse', 'elephant', 'sheep', 'cat', 'aeroplane', 'car', 'dog', 'motorbike',
'sofa', 'person', 'bicycle', 'bus', 'zebra', 'bird', 'cow', 'bear', 'tvmonitor',
'train', 'giraffe', 'boat'],
'Count': [61, 1061, 245, 106, 131, 139, 124, 169, 80, 117, 294, 71, 75, 89, 85, 106, 124, 73, 99, 86]
}
# Create DataFrames
train_df = pd.DataFrame(train_data)
val_df = pd.DataFrame(val_data)
# Merge training and validation DataFrames
train_df['Dataset'] = 'Training'
val_df['Dataset'] = 'Validation'
combined_df = pd.concat([train_df, val_df], ignore_index=True)
# Define classes of interest (animals, vehicles, birds, and people)
classes_of_interest = [
'aeroplane', 'bicycle', 'bird', 'boat',
'bus', 'car', 'cat', 'cow', 'dog',
'horse', 'motorbike', 'person', 'sheep', 'sofa', 'train'
]
# Filter the combined DataFrame for classes of interest
highlighted_df = combined_df[combined_df['Object Class'].isin(classes_of_interest)]
# Combine counts for the pie chart
combined_counts = highlighted_df.groupby('Object Class')['Count'].sum().reset_index()
# Set the style of seaborn
sns.set(style='whitegrid')
# 1. Combined Pie Chart for Class Distribution
plt.figure(figsize=(10, 10))
plt.pie(combined_counts['Count'],
labels=combined_counts['Object Class'],
autopct='%1.1f%%', startangle=140)
plt.title('Combined Distribution of Training and Validation Images per Class')
plt.axis('equal') # Equal aspect ratio ensures that pie chart is circular.
plt.show()
# Set the style of seaborn
sns.set(style='whitegrid')
# Grouped Bar Chart with Values on Top for Filtered Classes
plt.figure(figsize=(16, 8))
bar_plot = sns.barplot(x='Object Class', y='Count', hue='Dataset', data=filtered_combined_df, palette='viridis')
plt.title('Grouped Bar Chart of Object Instances by Class (Filtered)')
plt.xlabel('Object Class')
plt.ylabel('Count of Images')
plt.xticks(rotation=45)
plt.legend(title='Dataset')
# Adding values on top of each bar
for p in bar_plot.patches:
bar_plot.annotate(f'{int(p.get_height())}',
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='bottom',
fontsize=10, color='black',
xytext=(0, 5), # Text offset
textcoords='offset points')
plt.show()
import os
# Define the paths to the training and validation image directories
train_image_dir = '/kaggle/working/VOCdevkit/VOC2012/train/images'
val_image_dir = '/kaggle/working/VOCdevkit/VOC2012/val/images'
# Count the number of images in the training directory
train_images = [f for f in os.listdir(train_image_dir) if f.endswith('.jpg')]
num_train_images = len(train_images)
# Count the number of images in the validation directory
val_images = [f for f in os.listdir(val_image_dir) if f.endswith('.jpg')]
num_val_images = len(val_images)
# Print the results
print(f'Total number of training images: {num_train_images}')
print(f'Total number of validation images: {num_val_images}')
Total number of training images: 4403 Total number of validation images: 580
yaml_content = """
train: /kaggle/working/VOCdevkit/VOC2012/train/images
val: /kaggle/working/VOCdevkit/VOC2012/val/images
nc: 20 # number of classes
names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
'train', 'tvmonitor']
"""
# Save the YAML content to a file
yaml_path = "/kaggle/working/voc2012_subset.yaml"
with open(yaml_path, "w") as f:
f.write(yaml_content)
#Disable wandb
os.environ['WANDB_MODE'] = 'disabled'
model = YOLO('yolov8n.pt')
model.train(data=yaml_path, epochs=25, imgsz=256, batch=20)
Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...
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Ultralytics YOLOv8.2.101 🚀 Python-3.10.14 torch-2.4.0 CUDA:0 (Tesla T4, 15095MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=/kaggle/working/voc2012_subset.yaml, epochs=25, time=None, patience=100, batch=20, imgsz=256, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train
Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...
100%|██████████| 755k/755k [00:00<00:00, 13.8MB/s] 2024-09-26 12:54:40,755 INFO util.py:124 -- Outdated packages: ipywidgets==7.7.1 found, needs ipywidgets>=8 Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output. 2024-09-26 12:54:42,010 INFO util.py:124 -- Outdated packages: ipywidgets==7.7.1 found, needs ipywidgets>=8 Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.
Overriding model.yaml nc=80 with nc=20
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 755212 ultralytics.nn.modules.head.Detect [20, [64, 128, 256]]
Model summary: 225 layers, 3,014,748 parameters, 3,014,732 gradients, 8.2 GFLOPs
Transferred 319/355 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /kaggle/working/VOCdevkit/VOC2012/train/labels... 5481 images, 0 backgrounds, 0 corrupt: 100%|██████████| 5481/5481 [00:05<00:00, 914.03it/s]
train: New cache created: /kaggle/working/VOCdevkit/VOC2012/train/labels.cache albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
/opt/conda/lib/python3.10/site-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 1.4.16 (you have 1.4.15). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.
check_for_updates()
/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
self.pid = os.fork()
val: Scanning /kaggle/working/VOCdevkit/VOC2012/val/labels... 1265 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1265/1265 [00:01<00:00, 864.05it/s]
val: New cache created: /kaggle/working/VOCdevkit/VOC2012/val/labels.cache
Plotting labels to runs/detect/train/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.000417, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.00046875), 63 bias(decay=0.0) TensorBoard: model graph visualization added ✅ Image sizes 256 train, 256 val Using 2 dataloader workers Logging results to runs/detect/train Starting training for 25 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/25 0.612G 1.28 3.375 1.269 6 256: 100%|██████████| 275/275 [00:33<00:00, 8.22it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:07<00:00, 4.35it/s]
all 1265 3335 0.54 0.388 0.395 0.276
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/25 0.512G 1.313 2.187 1.29 5 256: 100%|██████████| 275/275 [00:29<00:00, 9.21it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.90it/s]
all 1265 3335 0.578 0.449 0.466 0.303
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/25 0.547G 1.279 1.944 1.3 8 256: 100%|██████████| 275/275 [00:27<00:00, 9.84it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.88it/s]
all 1265 3335 0.565 0.439 0.45 0.294
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/25 0.558G 1.277 1.895 1.296 2 256: 100%|██████████| 275/275 [00:28<00:00, 9.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.90it/s]
all 1265 3335 0.595 0.453 0.479 0.311
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/25 0.556G 1.242 1.806 1.275 5 256: 100%|██████████| 275/275 [00:28<00:00, 9.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.94it/s]
all 1265 3335 0.639 0.466 0.508 0.335
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/25 0.528G 1.228 1.75 1.266 4 256: 100%|██████████| 275/275 [00:27<00:00, 9.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.85it/s]
all 1265 3335 0.606 0.477 0.52 0.339
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/25 0.545G 1.215 1.709 1.256 4 256: 100%|██████████| 275/275 [00:28<00:00, 9.67it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.86it/s]
all 1265 3335 0.599 0.474 0.506 0.332
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/25 0.531G 1.197 1.658 1.251 2 256: 100%|██████████| 275/275 [00:27<00:00, 9.85it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.86it/s]
all 1265 3335 0.656 0.482 0.532 0.356
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/25 0.562G 1.188 1.64 1.243 11 256: 100%|██████████| 275/275 [00:28<00:00, 9.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.82it/s]
all 1265 3335 0.657 0.472 0.523 0.356
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/25 0.577G 1.161 1.579 1.232 2 256: 100%|██████████| 275/275 [00:28<00:00, 9.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.64it/s]
all 1265 3335 0.689 0.484 0.549 0.371
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/25 0.543G 1.143 1.538 1.22 2 256: 100%|██████████| 275/275 [00:27<00:00, 9.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.50it/s]
all 1265 3335 0.614 0.508 0.548 0.377
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/25 0.528G 1.136 1.524 1.217 3 256: 100%|██████████| 275/275 [00:28<00:00, 9.67it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.82it/s]
all 1265 3335 0.655 0.516 0.561 0.385
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/25 0.573G 1.129 1.507 1.213 16 256: 100%|██████████| 275/275 [00:28<00:00, 9.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.85it/s]
all 1265 3335 0.662 0.504 0.563 0.393
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/25 0.575G 1.118 1.465 1.203 3 256: 100%|██████████| 275/275 [00:28<00:00, 9.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.95it/s]
all 1265 3335 0.701 0.516 0.574 0.397
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/25 0.537G 1.112 1.453 1.205 4 256: 100%|██████████| 275/275 [00:28<00:00, 9.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.95it/s]
all 1265 3335 0.693 0.524 0.573 0.394
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock. self.pid = os.fork() /opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock. self.pid = os.fork()
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/25 0.543G 1.069 1.318 1.147 3 256: 100%|██████████| 275/275 [00:28<00:00, 9.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 6.01it/s]
all 1265 3335 0.686 0.527 0.578 0.396
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/25 0.537G 1.054 1.226 1.141 1 256: 100%|██████████| 275/275 [00:27<00:00, 9.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.83it/s]
all 1265 3335 0.645 0.553 0.587 0.411
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/25 0.526G 1.024 1.183 1.121 3 256: 100%|██████████| 275/275 [00:27<00:00, 10.06it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.86it/s]
all 1265 3335 0.745 0.517 0.603 0.425
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/25 0.552G 1.014 1.149 1.119 8 256: 100%|██████████| 275/275 [00:27<00:00, 9.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.97it/s]
all 1265 3335 0.703 0.547 0.611 0.431
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/25 0.535G 0.9978 1.12 1.106 2 256: 100%|██████████| 275/275 [00:27<00:00, 9.93it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.86it/s]
all 1265 3335 0.733 0.534 0.611 0.431
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/25 0.552G 0.9831 1.079 1.101 4 256: 100%|██████████| 275/275 [00:27<00:00, 9.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 6.07it/s]
all 1265 3335 0.719 0.542 0.612 0.429
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/25 0.531G 0.9657 1.046 1.088 2 256: 100%|██████████| 275/275 [00:27<00:00, 9.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.86it/s]
all 1265 3335 0.743 0.542 0.618 0.439
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/25 0.549G 0.9586 1.022 1.081 1 256: 100%|██████████| 275/275 [00:27<00:00, 9.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.83it/s]
all 1265 3335 0.731 0.547 0.622 0.442
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/25 0.535G 0.9425 1.006 1.078 3 256: 100%|██████████| 275/275 [00:27<00:00, 10.10it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.51it/s]
all 1265 3335 0.756 0.541 0.627 0.442
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/25 0.543G 0.937 0.979 1.071 1 256: 100%|██████████| 275/275 [00:27<00:00, 9.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:05<00:00, 5.59it/s]
all 1265 3335 0.767 0.546 0.633 0.45 25 epochs completed in 0.245 hours. Optimizer stripped from runs/detect/train/weights/last.pt, 6.2MB Optimizer stripped from runs/detect/train/weights/best.pt, 6.2MB Validating runs/detect/train/weights/best.pt... Ultralytics YOLOv8.2.101 🚀 Python-3.10.14 torch-2.4.0 CUDA:0 (Tesla T4, 15095MiB) Model summary (fused): 168 layers, 3,009,548 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 32/32 [00:06<00:00, 4.90it/s]
all 1265 3335 0.769 0.546 0.633 0.45
aeroplane 77 117 0.896 0.667 0.769 0.581
bicycle 65 89 0.819 0.685 0.769 0.555
bird 86 139 0.727 0.536 0.578 0.384
boat 55 106 0.764 0.396 0.514 0.296
bottle 73 124 0.695 0.331 0.412 0.28
bus 40 61 0.827 0.689 0.764 0.628
car 131 245 0.806 0.478 0.594 0.418
cat 119 131 0.753 0.756 0.8 0.666
chair 130 294 0.581 0.347 0.422 0.267
cow 31 86 0.766 0.488 0.53 0.362
diningtable 63 71 0.91 0.568 0.698 0.508
dog 143 169 0.714 0.556 0.665 0.494
horse 50 73 0.831 0.676 0.764 0.544
motorbike 62 85 0.81 0.612 0.77 0.58
person 466 1061 0.873 0.604 0.74 0.488
pottedplant 63 106 0.685 0.443 0.493 0.267
sheep 31 99 0.717 0.475 0.542 0.376
sofa 64 75 0.581 0.535 0.571 0.435
train 62 80 0.836 0.637 0.731 0.538
tvmonitor 66 124 0.781 0.435 0.534 0.341
Speed: 0.0ms preprocess, 0.5ms inference, 0.0ms loss, 1.1ms postprocess per image
Results saved to runs/detect/train
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7a25ac0cc220>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,
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[ 0.91011, 0.91011, 0.88764, ..., 0, 0, 0],
[ 0.83453, 0.83453, 0.81295, ..., 0, 0, 0],
...,
[ 0.96, 0.96, 0.94667, ..., 0, 0, 0],
[ 0.9375, 0.9375, 0.8875, ..., 0, 0, 0],
[ 0.80645, 0.80645, 0.79032, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: 0.46858879443620394
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.58139, 0.5553, 0.38401, 0.29571, 0.28037, 0.62752, 0.41753, 0.6657, 0.2666, 0.36159, 0.50827, 0.49417, 0.54365, 0.57952, 0.48771, 0.26702, 0.37598, 0.43531, 0.53755, 0.34135])
names: {0: 'aeroplane', 1: 'bicycle', 2: 'bird', 3: 'boat', 4: 'bottle', 5: 'bus', 6: 'car', 7: 'cat', 8: 'chair', 9: 'cow', 10: 'diningtable', 11: 'dog', 12: 'horse', 13: 'motorbike', 14: 'person', 15: 'pottedplant', 16: 'sheep', 17: 'sofa', 18: 'train', 19: 'tvmonitor'}
plot: True
results_dict: {'metrics/precision(B)': 0.7685646999560187, 'metrics/recall(B)': 0.5456727119436628, 'metrics/mAP50(B)': 0.633067430969436, 'metrics/mAP50-95(B)': 0.4503133903769559, 'fitness': 0.46858879443620394}
save_dir: PosixPath('runs/detect/train')
speed: {'preprocess': 0.03866659322746186, 'inference': 0.48821905384893, 'loss': 0.00034302119681015313, 'postprocess': 1.0965969251549763}
task: 'detect'
results = model.val()
print(results)
Ultralytics YOLOv8.2.101 🚀 Python-3.10.14 torch-2.4.0 CUDA:0 (Tesla T4, 15095MiB) Model summary (fused): 168 layers, 3,009,548 parameters, 0 gradients, 8.1 GFLOPs
val: Scanning /kaggle/working/VOCdevkit/VOC2012/val/labels.cache... 1265 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1265/1265 [00:00<?, ?it/s]
/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
self.pid = os.fork()
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 64/64 [00:09<00:00, 6.99it/s]
all 1265 3335 0.768 0.546 0.633 0.449
aeroplane 77 117 0.914 0.667 0.773 0.583
bicycle 65 89 0.819 0.685 0.769 0.549
bird 86 139 0.708 0.54 0.579 0.383
boat 55 106 0.76 0.396 0.513 0.297
bottle 73 124 0.693 0.347 0.406 0.274
bus 40 61 0.826 0.689 0.764 0.625
car 131 245 0.81 0.478 0.593 0.416
cat 119 131 0.762 0.759 0.802 0.665
chair 130 294 0.58 0.347 0.422 0.267
cow 31 86 0.766 0.488 0.533 0.362
diningtable 63 71 0.91 0.566 0.698 0.508
dog 143 169 0.715 0.562 0.666 0.495
horse 50 73 0.832 0.671 0.764 0.536
motorbike 62 85 0.811 0.612 0.77 0.581
person 466 1061 0.874 0.605 0.741 0.488
pottedplant 63 106 0.68 0.434 0.491 0.267
sheep 31 99 0.721 0.47 0.543 0.376
sofa 64 75 0.581 0.536 0.567 0.43
train 62 80 0.825 0.647 0.737 0.538
tvmonitor 66 124 0.779 0.427 0.533 0.339
Speed: 0.1ms preprocess, 1.4ms inference, 0.0ms loss, 1.3ms postprocess per image
Results saved to runs/detect/train2
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7a26c37664d0>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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[ 0.91011, 0.91011, 0.88764, ..., 0, 0, 0],
[ 0.83453, 0.83453, 0.81295, ..., 0, 0, 0],
...,
[ 0.96, 0.96, 0.94667, ..., 0, 0, 0],
[ 0.925, 0.925, 0.8875, ..., 0, 0, 0],
[ 0.80645, 0.80645, 0.79032, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: 0.4674733799631356
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.58342, 0.54946, 0.38337, 0.29721, 0.27378, 0.62499, 0.41644, 0.66467, 0.2671, 0.36177, 0.50782, 0.49517, 0.53593, 0.5805, 0.48838, 0.26749, 0.37629, 0.43041, 0.53814, 0.33891])
names: {0: 'aeroplane', 1: 'bicycle', 2: 'bird', 3: 'boat', 4: 'bottle', 5: 'bus', 6: 'car', 7: 'cat', 8: 'chair', 9: 'cow', 10: 'diningtable', 11: 'dog', 12: 'horse', 13: 'motorbike', 14: 'person', 15: 'pottedplant', 16: 'sheep', 17: 'sofa', 18: 'train', 19: 'tvmonitor'}
plot: True
results_dict: {'metrics/precision(B)': 0.7683461902255229, 'metrics/recall(B)': 0.5463380525227478, 'metrics/mAP50(B)': 0.6331675619412268, 'metrics/mAP50-95(B)': 0.44906291529890324, 'fitness': 0.4674733799631356}
save_dir: PosixPath('runs/detect/train2')
speed: {'preprocess': 0.0678882297319857, 'inference': 1.384352318383017, 'loss': 0.0010567691486343565, 'postprocess': 1.286023030639166}
task: 'detect'
import os
file_path = "runs/detect/train/weights/best.pt"
if os.path.exists(file_path):
print("File exists!")
else:
print("File does not exist.")
File exists!
from ultralytics import YOLO
from PIL import Image
# Load the model
model = YOLO('runs/detect/train/weights/best.pt')
# Print results
results = model.predict('/kaggle/input/to-test/000000000086.jpg') # Replace with your image path
image 1/1 /kaggle/input/to-test/000000000086.jpg: 256x224 1 motorbike, 2 persons, 43.8ms Speed: 1.0ms preprocess, 43.8ms inference, 1.7ms postprocess per image at shape (1, 3, 256, 224)
metrics = {'metrics/precision(B)': 0.7683461902255229, 'metrics/recall(B)': 0.5463380525227478, 'metrics/mAP50(B)': 0.6331675619412268, 'metrics/mAP50-95(B)': 0.44906291529890324}
for metric, value in metrics.items():
print(f"{metric}: {value}")
metrics/precision(B): 0.7683461902255229 metrics/recall(B): 0.5463380525227478 metrics/mAP50(B): 0.6331675619412268 metrics/mAP50-95(B): 0.44906291529890324
# Read the image
img_path = '/kaggle/input/to-test/000000000086.jpg'
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert from BGR to RGB
# Draw bounding boxes
for result in results:
for box in result.boxes:
# Get coordinates and confidence
x1, y1, x2, y2 = box.xyxy[0] # Only the coordinates
conf = box.conf[0] # Confidence
cls = box.cls[0] # Class index
label = f'{model.names[int(cls)]} {conf:.2f}'
cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)
cv2.putText(img, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
# Display the image with bounding boxes
plt.figure(figsize=(10, 10))
plt.imshow(img)
plt.axis('off') # Hide axes
plt.show()
import os
import cv2
import matplotlib.pyplot as plt
# Path to the folder containing images
folder_path = '/kaggle/input/testing/New folder'
# Loop through all files in the folder
for filename in os.listdir(folder_path):
if filename.endswith('.jpg'): # Check for jpg files
img_path = os.path.join(folder_path, filename)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Run inference
results = model.predict(img_path, conf=0.25)
# Draw bounding boxes
for result in results:
for box in result.boxes:
x1, y1, x2, y2 = box.xyxy[0]
conf = box.conf[0]
cls = box.cls[0]
label = f'{model.names[int(cls)]} {conf:.2f}'
cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 1)
cv2.putText(img, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2)
# Display the image with bounding boxes
plt.figure(figsize=(8, 8))
plt.imshow(img)
plt.axis('off')
plt.show()
image 1/1 /kaggle/input/testing/New folder/photo_5452071308935423640_y.jpg: 256x192 1 person, 8.5ms Speed: 2.3ms preprocess, 8.5ms inference, 1.7ms postprocess per image at shape (1, 3, 256, 192)
image 1/1 /kaggle/input/testing/New folder/horse_352290.jpg: 256x256 1 horse, 9.7ms Speed: 1.0ms preprocess, 9.7ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/cat_303219.jpg: 256x256 2 chairs, 2 persons, 1 sofa, 8.4ms Speed: 1.1ms preprocess, 8.4ms inference, 1.7ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/photo_5452071308935423509_y.jpg: 256x192 1 dog, 2 persons, 9.0ms Speed: 1.0ms preprocess, 9.0ms inference, 1.5ms postprocess per image at shape (1, 3, 256, 192)
image 1/1 /kaggle/input/testing/New folder/cat_115070.jpg: 256x256 2 cats, 1 pottedplant, 9.3ms Speed: 1.1ms preprocess, 9.3ms inference, 1.5ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/horse_139561.jpg: 256x256 2 horses, 9.4ms Speed: 1.1ms preprocess, 9.4ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/motorcycle_32997.jpg: 256x256 1 bicycle, 3 motorbikes, 3 persons, 1 pottedplant, 8.1ms Speed: 1.0ms preprocess, 8.1ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/photo_5452071308935423506_y.jpg: 256x192 1 bicycle, 2 persons, 9.6ms Speed: 0.9ms preprocess, 9.6ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 192)
image 1/1 /kaggle/input/testing/New folder/bird_303439.jpg: 256x256 12 birds, 1 sheep, 9.1ms Speed: 1.0ms preprocess, 9.1ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/000000000049.jpg: 256x224 1 horse, 2 persons, 9.7ms Speed: 1.0ms preprocess, 9.7ms inference, 1.5ms postprocess per image at shape (1, 3, 256, 224)
image 1/1 /kaggle/input/testing/New folder/000000000086.jpg: 256x224 1 motorbike, 2 persons, 9.5ms Speed: 1.0ms preprocess, 9.5ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 224)
image 1/1 /kaggle/input/testing/New folder/bird_98601.jpg: 256x256 2 birds, 4 boats, 2 persons, 9.3ms Speed: 1.0ms preprocess, 9.3ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/000000016513.jpg: 192x256 1 bus, 10.8ms Speed: 1.3ms preprocess, 10.8ms inference, 1.5ms postprocess per image at shape (1, 3, 192, 256)
image 1/1 /kaggle/input/testing/New folder/WIN_20240130_18_35_35_Pro.jpg: 160x256 3 persons, 9.7ms Speed: 0.9ms preprocess, 9.7ms inference, 1.6ms postprocess per image at shape (1, 3, 160, 256)
image 1/1 /kaggle/input/testing/New folder/bird_57830.jpg: 256x256 2 boats, 8.9ms Speed: 1.0ms preprocess, 8.9ms inference, 1.6ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/cat_90331.jpg: 256x256 2 bottles, 1 cat, 1 person, 8.5ms Speed: 1.1ms preprocess, 8.5ms inference, 1.5ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/000000000165.jpg: 224x256 2 persons, 9.3ms Speed: 1.0ms preprocess, 9.3ms inference, 1.7ms postprocess per image at shape (1, 3, 224, 256)
image 1/1 /kaggle/input/testing/New folder/000000000081.jpg: 192x256 1 aeroplane, 9.2ms Speed: 0.8ms preprocess, 9.2ms inference, 1.5ms postprocess per image at shape (1, 3, 192, 256)
image 1/1 /kaggle/input/testing/New folder/horse_336172.jpg: 256x224 1 horse, 1 person, 9.1ms Speed: 0.9ms preprocess, 9.1ms inference, 1.4ms postprocess per image at shape (1, 3, 256, 224)
image 1/1 /kaggle/input/testing/New folder/horse_295194.jpg: 256x256 3 horses, 2 persons, 9.6ms Speed: 1.1ms preprocess, 9.6ms inference, 1.7ms postprocess per image at shape (1, 3, 256, 256)
image 1/1 /kaggle/input/testing/New folder/photo_5452071308935423508_y.jpg: 256x192 1 cow, 10 sheeps, 9.4ms Speed: 1.0ms preprocess, 9.4ms inference, 1.5ms postprocess per image at shape (1, 3, 256, 192)
image 1/1 /kaggle/input/testing/New folder/photo_5452071308935423505_y.jpg: 256x192 1 boat, 6 persons, 8.2ms Speed: 1.0ms preprocess, 8.2ms inference, 1.5ms postprocess per image at shape (1, 3, 256, 192)